Carotid Intima-Media Thickness Progression and Risk of Vascular Events in People With Diabetes: Results From the PROG-IMT Collaboration
Bibliographic record
Abstract
OBJECTIVE: Carotid intima-media thickness (CIMT) is a marker of subclinical organ damage and predicts cardiovascular disease (CVD) events in the general population. It has also been associated with vascular risk in people with diabetes. However, the association of CIMT change in repeated examinations with subsequent CVD events is uncertain, and its use as a surrogate end point in clinical trials is controversial. We aimed at determining the relation of CIMT change to CVD events in people with diabetes. RESEARCH DESIGN AND METHODS: In a comprehensive meta-analysis of individual participant data, we collated data from 3,902 adults (age 33-92 years) with type 2 diabetes from 21 population-based cohorts. We calculated the hazard ratio (HR) per standard deviation (SD) difference in mean common carotid artery intima-media thickness (CCA-IMT) or in CCA-IMT progression, both calculated from two examinations on average 3.6 years apart, for each cohort, and combined the estimates with random-effects meta-analysis. RESULTS: Average mean CCA-IMT ranged from 0.72 to 0.97 mm across cohorts in people with diabetes. The HR of CVD events was 1.22 (95% CI 1.12-1.33) per SD difference in mean CCA-IMT, after adjustment for age, sex, and cardiometabolic risk factors. Average mean CCA-IMT progression in people with diabetes ranged between -0.09 and 0.04 mm/year. The HR per SD difference in mean CCA-IMT progression was 0.99 (0.91-1.08). CONCLUSIONS: Despite reproducing the association between CIMT level and vascular risk in subjects with diabetes, we did not find an association between CIMT change and vascular risk. These results do not support the use of CIMT progression as a surrogate end point in clinical trials in people with diabetes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.016 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".